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www.inbroadcast.com | Vol: 8 - Issue 7 | August 2018
InForm
Using AI with MAMs
Jay Batista, General Manager, US Operations, Tedial,
asks: "Does the reality justify the hype?"...
O
ur industry is buzzing about
Artificial Intelligence (AI) and
machine learning tools. Walk the aisles
at trade shows and you'll notice every
MAM vendor is touting its AI partners,
tools and integrations. But behind all
the brouhaha, does the reality justify
the hype? Is there really an effective
application or workflow that will result
in more profits for your organisation?
Utilising AI For Your Business
Like any tool, having a machine
that is capable of learning and
accomplishing menial and repetitive
tasks is only useful if you can identify
a set of tasks that are necessary to
your business operations. To build a
cost-effective AI tool that adds value
to your operation, you must have
clearly defined objectives. The more
focused the tasks, the more profitable
and efficient the AI application. Ask
yourself: Are you looking to maximise
the monetisation of your multi-year
archive? Are you seeking to annotate
media for international distribution
versioning? Do you have a specific
internal data model that needs to be
applied to all incoming media?
AI Applications
Machine learning married to MAMs
can do a number of tasks well and one
of the best applications is to annotate
the media with machine-generated
metadata. A key function of learning
software is image recognition. Major
cloud-based systems have instructed
their tools to provide facial recognition
for celebrity identification, yet these
systems can also be optimised for
image analysis and identification of
objects and people, and maximised to
annotate scene changes, on-screen
graphics, and identify recognisable
locations - all hugely important in
monetising archives.
For
international
versions,
compliance monitoring is critical,
demanding an application that
can automate searches for nudity,
smoking, drinking or violence for
editing. These systems can be trained
to make product placement notations,
in case your organisation has the
rights to remove or sell/replace these
brand promotions.
Another specific application that
interests end-users is audio-totext proxy annotation, so key word
searches can take you directly to a
precise location in the media. Audio
compliance monitoring is available
for proscribed language or words, and
key word notations such as mentions
of people by name or position are
sometimes applied to media, whether
it is news, sports events or long-form
content. There is even interest in
using AI to recognise "sentiment" and
annotate media to focus and target
advertisements. And today's tools now
support animation.
Jay Batista,
General Manager,
US Operations
On-Premises vs Cloud Systems
Only your business can determine
the applications that will drive value.
Once you have found the business
reason to use the machine learning
tools, then it is time to decide if an
on-premises solution is better than
a cloud-based tool: If media security
and/or a reliable speed of response
is important, then the on-premises
tools better support your business
requirements. Cloud systems leverage
broader and deeper trained engines
with more frequent updates that
employ the benefits of "crowdsourcing" to learn and apply the
analysis in better ways.
If AI is developed properly,
it can be exceedingly valuable
Another issue to be considered when
selecting machine learning tools is
whether an AI platform with hundreds
of software tools, each maximised
for a specific chore, is better for your
needs versus specific machine learning
tools applied to your media assets. The
former allows your business to benefit
from a wide set of tools, while the
latter allows your company to focus a
particular system on specific, company
unique responses.
AI Needs To Be 'Taught'
There are caveats to this brave new
world of learning software tools. It
is really key to make your executive
team aware that AI doesn't know what
it doesn't know. Generic applications
are supported by the big cloud
providers and the platforms, but those
tools may not result in a payback
on your investment and application.
Industry specific, and especially
unique business-focused, data model
applications that bring your company
value may not be available. And most
important, these specific tools that
bring the most value must be "taught."
Machine Learning tools are empty
shells until they are loaded with data
and comparative examples.
Is all the hype surrounding AI
tethered to reality? AI is a work
in progress in its infancy. If it is
developed properly and applied
intelligently (pun intended), it can be
exceedingly valuable. Watch out, Hal!
You've got competition.